Despite its potential, there are several challenges to the widespread adoption of AI in histology:
Data Quality: High-quality, annotated datasets are crucial for training effective AI models. Variability in staining techniques and slide preparation can impact data quality. Interpretability: AI models, especially deep learning networks, can be considered "black boxes" with limited transparency in their decision-making processes. This lack of interpretability can be a barrier to trust and adoption. Regulatory Hurdles: Ensuring compliance with medical regulations and obtaining approval from health authorities can be a complex and time-consuming process. Integration with Clinical Workflow: Seamlessly integrating AI tools into existing clinical workflows without disrupting the routine of pathologists is essential for successful implementation.
